Single-Cell Analysis of Normal and Perturbed Early T-Cell

However, the platform and method of choice greatly influence the sensitivity, dropout rate and technical noise of genes being measured, as well as the throughput of the tests (Svensson et al., 2017). They demonstrated a robust prediction between TFs and target genes using single cell data (Aibar et al., 2017). In addition, T cell progenitors can migrate to the thymus from different hematopoietic progenitor states (CLP and LMPP) (Saran et al., 2010) (Fig. 1a).

Single-cell transcriptional profiling by RNAseq (scRNA-seq) has transformed our understanding of hematopoietic differentiation and heterogeneity (Boudil et al., 2013; Expression of a Bcl11b-YFP knockout reporter (Kueh et al., 2016). distinguishable (YFP-) from newly committed DN2a cells (YFP+) was used to mark the commitment milestone (Fig. 1b,c; Table S1). As expected (Kueh et al., 2016), the commitment gene of T-lineage Bcl11b was activated exclusively in Tcf7-expressing cells, and almost completely within the DN2 stage (Fig. 2f).

To elucidate developmental flows between populations in the ETP-DN2 transition, we used RNA velocity analysis (Velocyto) (La Manno et al., 2018) (Figure DDRtree (Qiu et al., 2017a) was used to obtain the associated developmental trajectory and pseudo-temporal stage cells (Fig. 5c–e, Fig. S8). This is likely a reflection of the exceptional stability of the PU.1 protein, as previously reported (Kueh et al., 2013).

B6.Bcl11byfp/yfp reporter ( Kueh et al., 2016 ) mice were used for bulk RNAseq analysis, in vitro developmental assays, and ETP subpopulation Cell Hashing 10X scRNA-seq. For live imaging experiments, a derivative of the OP9-DL1 cells was used, OP9-DL1-delGFP1, in which the GFP tag was removed in the cell line by Cas9-mediated disruption as described elsewhere (Olariu et al., 2021). Where the Bcl11b-YFP allele is present, the onset of Bcl11b-YFP expression differentiates T lineage-committed DN2a cells from earlier, uncommitted DN2a cells ( Kueh et al., 2016 ).

Our recent study has characterized the fine gene expression pattern of mouse pro-T cells before lineage commitment (Zhou et al., 2019). Bcl11b is not expressed in cells until they reach the commitment transition, after which it is expressed in all committed T lineage cells (Kueh et al., 2016; Zhou et al., 2019). Many regulatory network changes appear to precede the T-lineage commitment decision (Zhou et al., 2019), but the basis of their regulation is poorly understood.

Here we used CRISPR/Cas9 in the KO context, similar to the original perturbseq setup ( Dixit et al., 2016 ). To further minimize batch variations, we multiplexed multiple batches of biological replicates using antibody-conjugated cell hashing technique (Stoeckius et al., 2018). Loss of Gata3 immediately promoted all myeloid programs, including upregulating Irf8 (DC) and C/EBP family genes (MF), while supporting proliferation (Olsson et al., 2016).

All genes plotted are from a list of selected important regulatory gene lists described in the previous study (Zhou et al., 2019).

Figure 1. The Classic View of Hematopoiesis Hierarchy and the Regulators (adapted from Orkin and Zon, 2008)

SUPPLEMENTARY MATERIALS

Whole-cell and single-cell RNA-seq analysis of in vivo and ex-vivo derived early T cells. Surface and expression profiles of WT and Bcl11b KO single-cell samples (labeled as 'FF' for the flx/flx Bcl11b homozygous locus). Single-cell RNA-seq (10X Chrom V3) on Bcl11b Hashim's LSK of cells from Bcl11b WT and KO animals was sampled, split into 6-7k cells/tube, and stored in liquid nitrogen as described above above (individual animals were not pooled) .

Perturb-seq: Dissecting molecular circuits with scalable single-cell RNA profiling from merged genetic screens. Single-cell analysis reveals the dynamics of regulatory gene expression leading to lineage commitment in early T cell development. Another challenge in the field of single-cell analysis is the so-called 'unified analysis'.

Around the time we published our early T-line single cell study in mouse systems (Zhou et al. 2019, presented in Chapter 2), several other groups also published similar mouse and human single cell profiles, focusing on different stages and cell populations. In mouse systems, a comprehensive, dynamic single-cell analysis of hematopoietic and stromal cells during thymic organogenesis in the mouse fetus was published by Kernfeld et al. Perhaps, in the future, the easiest and simplest use of 'prior knowledge' is pre-experimental data analysis with publicly available single cell data from similar cell types, regardless of the platform of data acquisition.

The main use of single-cell analysis up to this point has focused on descriptive analytics, e.g. However, some newer tools promise to further integrate multimodality single-cell data (eg, scATAC-seq) or prior knowledge of GRN topology, thereby exploiting the better predictive power of single-cell analysis. It should be noted that Dibaeinia and Sinha, 2020 demonstrated in silico disruption of some TFs of interest, exploiting our single-cell seqFISH data presented in Chapter 2 and Zhou et al.

Massively parallel single-cell RNA sequencing for label-free dissection of tissues into cell types. Single-cell RNA-seq mapping of human thymopoiesis reveals lineage specification trajectories and a commitment spectrum in T cell development. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks.

Quantifying the trade-off between sequencing depth and cell number in single-cell RNA-seq (Genomics). Detection of rare cells by single-molecule RNA FISH-guided single-cell RNA sequencing.